Abstract
We present a system for interactive in situ visualization of large particle simulations, suitable for general CPU-based HPC architectures. As simulations grow in scale, in situ methods are needed to alleviate IO bottlenecks and visualize data at full spatio-temporal resolution. We use a lightweight loosely-coupled layer serving distributed data from the simulation to a data-parallel renderer running in separate processes. Leveraging the OSPRay ray tracing framework for visualization and balanced P-k-d trees, we can render simulation data in real-time, as they arrive, with negligible memory overhead. This flexible solution allows users to perform exploratory in situ visualization on the same computational resources as the simulation code, on dedicated visualization clusters or remote workstations, via a standalone rendering client that can be connected or disconnected as needed. We evaluate this system on simulations with up to 227M particles in the LAMMPS and Uintah computational frameworks, and show that our approach provides many of the advantages of tightly-coupled systems, with the flexibility to render on a wide variety of remote and coprocessing resources.
Highlights
In the coming era of exascale computing, simulations will produce data far in excess of what can be effectively archived in parallel file systems
One has the choice of tightly-coupled visualization embedded directly in the simulation code and running on the compute resource [10], or asynchronous loosely-coupled approaches that forward data from the simulation into separate visualization processes (e.g. [11]), either on the same machine or a different cluster
In situ rendering or analysis requires consideration of distributed data spread across multiple simulation nodes and potentially too large to be marshaled to a single node for processing
Summary
In the coming era of exascale computing, simulations will produce data far in excess of what can be effectively archived in parallel file systems. In situ visualization can add additional compute cost to the simulation which may change its performance characteristics. It comes with the benefit of reducing or eliminating time spent in file IO. Much in situ research has emphasized sidestepping IO through specific analysis, data reduction or filtering [4, 5], optimizations to existing IO frameworks enabling scalable co-processing, streaming or offline visualization [6,7,8], and data forwarding mechanisms coupling simulations with production visualization software [9]. As simulations grow in scale, in situ analysis and data reduction have become popular tools in the computation-visualization workflow. These analyses are designed to operate alongside computation in batches. Fabian et al design adaptors for integration into existing simulations which hand off to a ParaView coprocessing API, allowing for a variety of algorithms to be implemented without requiring additional modification of the simulation for each algorithm [9]
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